1. Technical Field
The present invention relates to pattern-recognition computing and to interference-based optical computers.
2. Background Art
The primary background art for the present invention is the applicant's U.S. Pat. No. 5,093,802, which teaches the basics of interference-based computing. In that patent, computer-generated (synthetic) holograms are described as a means for producing the computer functions claimed. Devices that use interference-based computing have come to be called "photonic transistors" even though the process will operate using non-photonic energy forms.
In the February 1994 issue of the Computer Applications Journal appeared an article by the applicant which explains the basics of conventional computer generation of holograms as they apply to two-input photonic transistors.
Absent from the previous information on interference-based computing are several fundamental processes that the present invention utilizes. These include:
1. The computer generation of pattern-recognition image (fringe) component separators. PA1 2. The simultaneous recognition of multiple information-modulated input patterns. PA1 3. The separation of complex pattern combinations from dynamic images. PA1 4. The use of pattern recognition to produce computer logic. PA1 5. The use of special interference (from application Ser. No. 08/357,460) in pattern recognition. PA1 6. The use of frequency multiplexing of simultaneous logic functions (from application Ser. Nos. 08/357,460 and 08/454,070) in pattern recognition. PA1 7. The use of arrays of the full operational range of optical elements that go beyond the simple opaque, clear, or phase-adjusted ability of the individual pixels that make up ordinary computer-generated holograms. PA1 a) producing a first input wavefront of said at least one wavelength having a first pattern modulated with quantized information resulting in a first set of modulation states; PA1 b) producing at least one other input wavefront of at least one wavelength having at least one other pattern modulated with quantized information resulting in at least one other set of modulation states; PA1 c) combining said first and at least one other input wavefronts to produce at least one dynamic image having component parts, and PA1 d) separating energy from a subset of said component parts that have a computing function relationship with said quantized information to produce at least one output, PA1 said at least one wavelength includes a plurality of wavelengths, each of said plurality of wavelengths being independently modulated with quantized information having independent said computing relationships, PA1 thereby providing a method of frequency-multiplexed pattern-recognition computing. PA1 separating phase-varying energy from said subset of said component parts when said subset of said component parts has energy which varies in phase when different sets of said pattern sets are energized, PA1 thereby providing said at least one output having phase-modulated energy. PA1 separating energy which varies according to the tenets of special interference from said subset of said component parts when different sets of said pattern sets are energized, PA1 thereby providing a method of pattern-recognition computing using special interference. PA1 a first input capable of inputting a first modulated pattern; PA1 at least one other input for inputting at least one other modulated pattern; PA1 at least one output means; PA1 combining means for combining said first and at least one other modulated patterns to provide an output signal at said at least one output means, such that each modulation combination of said first and at least one other modulated patterns results in a discrete output, PA1 thereby providing a dynamic pattern-recognition computer. PA1 a) producing a first input model describing a first input wavefront having a first pattern modulated with quantized information which produces a first set of modulation states; PA1 b) producing at least one other input model describing at least one other input wavefront having at least one other pattern modulated with quantized information which produces at least one other set of modulation states, and PA1 c) producing a dynamic image model describing image components of at least one dynamic image by calculating energy distributions that result from combining said first input wavefront and said at least one other wavefront at the position of said dynamic image for combinations of said sets of modulation states, PA1 d) selecting, from said dynamic image model, image component subsets that are able to contribute to the production of an output waveform having a computing function relationship with said modulation states, PA1 e) producing a separator model describing an array of optical elements for separating energy from said image component subsets to produce at least one output, PA1 f) changing at least one of the following: (i) said first pattern description within said first input model, and (ii) said at least one other pattern description within said at least one other input model; and PA1 g) iterating steps c) through f) until a substantially optimized pattern-recognition configuration is achieved, PA1 a) producing a first input model describing (i) a first input wavefront modulated with quantized information which produces a first set of modulation states and (ii) a first array of input optical elements for impressing a first pattern on said first input wavefront; PA1 b) producing at least one other input model describing (i) at least one other input wavefront-modulated with quantized information which produces at least one other set of modulation states, and (ii) at least one other array of input optical elements for impressing at least one other pattern on said at least one other input wavefront, and PA1 c) producing a dynamic image model describing image components of at least one dynamic image by calculating energy distributions at the position of said dynamic image for combinations of said sets of modulation states that result from combining said first input wavefront modified by said first array of input optical elements and said at least one other wavefront as modified by said at least one other array of input optical elements, PA1 d) selecting, from said dynamic image model, image component subsets that are able to contribute to the production of an output waveform having a computing function relationship with said modulation states, PA1 e) producing a separator model describing an array of output optical elements for separating energy from said image component subsets to produce at least one output, PA1 f) changing at least one of the following: (1) said first array of input optics within said first input model, and (ii) said at least one other array of input optics within said at least one other input model, and PA1 g) iterating steps c) through f) until a substantially optimized pattern-recognition configuration is achieved, PA1 A=amplitude of the first beam. PA1 B=amplitude of the second beam. PA1 Theta=phase difference between the two beams. EQU Intensity=I=A.sup.2 +B.sup.2 +2AB Cos(Theta) PA1 A=B.sub.1 +U, and B=B.sub.2. The amplitude at location 1 will be B.sub.1 +U. PA1 "12. Active filter. PA1 a. Providing the first beam set with energy at a constant above-zero-level having at least one wavelength, and often several wavelengths; PA1 b. Switching wavelengths of the first beam set off and on to gate filtering of those individual wavelengths off and on; PA1 c. Providing the second beam set with energy at multiple wavelengths to be filtered, and PA1 d. Producing special interference with a subset of the multiple wavelengths matching the first beam set wavelengths and rejecting all other wavelengths, PA1 a. Providing the first beam set with its substantially constant above-zero level energy having at least one wavelength; PA1 b. Providing the second beam set with multiple wavelengths to be filtered, and PA1 c. Producing interference with a subset of those multiple wavelengths that match the at least one wavelength in the first beam set to divert energy of matching wavelengths away from the first location(s) and into the second location(s), PA1 a. Provide a plurality of active filers; PA1 b. Provide a frequency multiplexed beam set having a plurality of modulated wavelengths; PA1 c. Direct a portion of the frequency multiplexed beam set into the second (control) beam set of each filter, and PA1 d. Provide the first beam set of each filter with a different frequency of energy matching each of the plurality of modulated wavelengths,
Non-computing applications of pattern recognition are commonly produced by photographic and holographic techniques in the laboratory. While such methods work well for picking out static letters of the alphabet from a typewritten page, they are not well suited for use in functional active logic, digital computing, or signal processing.
The use of pattern recognition in digital computing requires at least two different patterns that are independently modulated with pattern-illuminating energy to make even an elementary logic device. The energy from the two patterns must be combined to form a dynamic image that changes continually as logic action proceeds. Additionally, there must be an image component separator in order to eliminate from the output any energy from component parts of the dynamic image that would not contribute to the output in a manner in harmony with the rules of logic for the particular device being made. The present invention surpasses the previous methods by providing these necessary things.
According to the teachings of the present invention, one could make some elementary logic devices by simply guessing which patterns might work well, and then producing a functioning logic device by trial and error. However, to optimize output signal levels and waveforms, a method is needed for determining exactly which pattern shapes work best, especially when the device utilizes a multitude of inputs and performs complex computing functions.
The present invention also teaches both a method of calculating pattern-recognition wavefronts, optics and systems as these apply to interference-based computing, and a method of optimizing the input patterns to provide optimal output waveforms from given input-modulation sequences.